Modelling and Simulation at PTS J. Kappen PTS Munich .
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Transcript of Modelling and Simulation at PTS J. Kappen PTS Munich .
Modelling and Simulation at PTS
J. Kappen PTS Munichwww.ptspaper.de
© PTS Munich
Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
© PTS Munich
Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
© PTS Munich
PTS Simulation Activities
Simulation activities since the late 80’ies PTS performs simulation activities in the field of
– Stock preparation– Wet End Chemistry– Coating– Water loop optimisation
Annual number of customer projects using simulation techniques– Approx. 15
Current number of research projects using simulation techniques– Approx. 15
© PTS Munich
IDEAS in PTS
Working with IDEAS since: 1998 Licences:
– 3 Network GOLD– 1 KODIAK– 1 Paper machine Library– 1 Pulp Library– 1 Advanced Control
Number of staff trained on IDEAS– 11
Other software packages in use– Matlab– Simulink– DOE: Modde
© PTS Munich
The PTS Approach on Simulation
PTS develops and commercializes a simulation supported process optimization product line:
PTS develops a product optimization tool to cut product cost and/or design superior product qualities:
C AP D
®
®
®
®
© PTS Munich
Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
© PTS Munich
Aspects of a Well Designed Water Circuit
stock preparation
paper machine
white water
tertiary circuit 1-5 m³/t
secondary circuit10-50 m³/t
primary circuit100-200 m³/t
fresh water
evaporation
fresh water plant
white water
clear waterclear water
recycled effluent
effluent treatment plant
waste
save all
effluent toreceiving waters
fibers
product
stock preparation
paper machine
white water
tertiary circuit 1-5 m³/t
secondary circuit10-50 m³/t
primary circuit100-200 m³/t
fresh water
evaporation
fresh water plant
white water
clear waterclear water
recycled effluent
effluent treatment plant
waste
save all
effluent toreceiving waters
fibers
product
no smell
low fiber and filler loss
low & constant contaminant loadings
no chemicals loss into wwtp
minimum variation of effluent flow
no water shortage at any time
low chemicals use
good circuit waterquality
minimum specific effluent volume
clean fibre stock
No additionalheating
minimum specific effluent volume
low & constant contaminant loadings
minimum variation of effluent flow
clean fibre stock
no water shortage at any time
No additionalheating
© PTS Munich
Key Problems in Mill Water Systems to be Solved by Simulation
What will happen if I reduce my specific effluent volume?
Is a loop separation beneficial to my process? How big should an integrated biological treatment
unit be? What will happen to the non degradable compounds
in the circuit water? How can heat be transferred to the paper machine? How much do I have to enlarge the water tanks in
the mill? What type of controls layout in the water circuits is
needed to smoothen the effluent flow trend?
© PTS Munich
Object: Loop
Status of the object– fully developed– steady state – fully coded into IDEAS
Operational features– ideal mixer– operation in pull or push mode possible– 1 pulp and 5 water/pulp inputs (1 draws in pull mode)– 1 pulp, 1 reject and 6 water output (1 overflow in push
mode; quality can be determined)– specific- and absolute heat in-and output, dissipation– component sources and sinks– “GUI” can be customized
© PTS Munich
Improved project performance through
-> reliabale methodology
-> simple model built
-> low effort for calibration
PTS Development: Loop Object
© PTS Munich
parameters:
water, solids, COD,
Ca, Cl, SO4, temperature
load input
freshwater
effluent
rejects
structure
of a typical
model
loop object
IDEAS Loop Model
© PTS Munich
Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
© PTS Munich
Prognosis of the impact of water system optimisation
Mill: MD Albbruck, Germany (LWC, MWC)
Application: Heat management and water loop setup optimisation
Example of a Paper Mill Heat Balance Optimisation
© PTS Munich
fresh water
WT 6 WT 5
NS DS
NSWasserturm
press
mech. fibertower
Oxi. Blt 1AOxi. Blt 1B
press
Red. Blt 6Red. Blt 4
presspress
PM 6PM 7 PM 5
fresh water (sealing water)
effluentfresh water (sealing)
warm fresh water
fresh water (heated)
clear waterPM6
effluent
effluent
clear water PM7clear water PM6
effluent
effluent
fresh water
Cricuit water
wood
SFWT 7WT 6
(kalt)
(heated) (heated)(cold)
press
WT 1
WT 1
fresh water
WT 7
WT 5
Red. Blt 5
clear water PM5
effluenteffluent
SF
Optimisation: Loop Separation and Heat Exchange
© PTS Munich
Model was built based on loop object Calibration using mill data Concept development Initial simulation runs to support discussion on concept
ideas Decision in favour of one concept taken Detailed simulation calculations:
- Balances for heat, COD and complexing agents
- Dimensioning of heat exchangers
Installation of heat exchangers Commissioning done by joint team MD and PTS
Project Steps
© PTS Munich
Improved separation of refining and bleaching department. Increase of temperature in reductive bleaching from 44°C
to 66°C as calculated. Increase of temperature in white water 42°C to 50°C as
calculated. Increased of whiteness by 0,5 - 1 point after bleaching. Peroxyde and hydrosulfite consumption reduced. Total cost for bleaching reduced by 7 to 12%, depending
on final whiteness. Significant gain in production rate as expected. Payback time < 0,7 years
Achieved Results
© PTS Munich
Results: Bleaching Stage
Peroxide dosage per ton
Bri
gh
tnes
s g
ain
before
after
© PTS Munich
Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
© PTS Munich
Statement 1: V(stock) = V(water buffer)
Statement 2: V(water dyn.) / V(stock dyn.)
= f (specific effluent volume)
Statement 3: To precisely define the buffer requirement, dynamic simulation calculations are
needed
0,01
0,1
1
10
0 5 10 15 20 25 30
Specific effluent volume [l/kg]
Dyn
amic
wa
ter
volu
mes
/
Dyn
amic
sto
ck v
olum
es
V
Actual situation
Approaches to the Dimensioning of Buffer Tanks
© PTS Munich
2
4
6
8
10
12
050100150200250
0:00
0:30
1:00
1:30
2:00
max min
buffer residence time(hrs.)
additionalwastewater volume
(l/kg)
volume of buffer m³
Dimensioning of Buffer Tanks
© PTS Munich
Hybrid modelling based upon:- Loop objects
- Targeted and other (relevant) tanks
- Data inputs (machine performance over time etc.)
High fidelity modelling only were required
Dynamic Models: Simulation Approach
© PTS Munich
Dynamic optimisation of in-mill water systems
Mill: Testliner
Application: Design of effluent flow control
Example of Designing an Effluent Flow Control
© PTS Munich
0,0
100,0
200,0
300,0
400,0
500,0
600,0
25.03.03 00:00 25.03.03 12:00 26.03.03 00:00 26.03.03 12:00 27.03.03 00:00
Basismodellohne geregelte Ausschleusung von Abwassermit geregelter Ausschleusung von Abwasser ohne Purgatormit geregelter Ausschleusung von Abwasser mit Purgator
1
2
3
4
scenario standard deviation1 as is situation 702 reduced specific effluent volume 1053 as 2 but with controlled effluent flow 504 as 3 but one water buffer tank taken out 35
Comparison of Various Options to Perform Effluent Flow Control
© PTS Munich
Dynamic optimisation of in-mill water systems
Mill: LWC
Application: Tank dimensioning in a SGW department
Example for the Dimensioning of Buffer Tanks
© PTS Munich
dynamic stock volume: 3200 m³
dynamic water volume: 550 m³
Required additional
chest volume:
Statement 1: V(stock) = V(water buffer) 2650 m³
Statement 2: V(water dyn.) / V(stock dyn) 900 m³
= f (specific effl. volume)
Statement 3: To precisely define the buffer 0 m³
requirement, dynamic simulation
calculations are needed
Dimensioning of Buffer Tanks
© PTS Munich
0
0,2
0,4
0,6
0,8
1
1,2
1:00 1:30 2:00 2:30 3:00 3:30 4:00 4:30 5:00
time [h]
pig
me
nt
co
nc
en
tra
tio
n [
%] 3
1
2
3
grade change
colour 1
colour 2
1 without emptying2 emptying stock preparation3 additional cleaning of papermachine
Model Upgrading: Simulation of Grade Change Dynamics
© PTS Munich
Fuzzy Control of Ideas Model
Ideas Process Model
Matlab Fuzzy Toolbox
Process Data
Control Parameters
© PTS Munich
Function of Matlab Link
Status of the object– fully developed– fully coded into IDEAS
Operational featuresAt start of Ideas Model: – Ideas opens Matlab and runs the initialization script.
Ideas is used to predefine Matlab workspace parameters.
During Simulation for every simulation step: – Output matrix is transmitted to Matlab workspace.– Simulation script is started within Matlab to perform
calculations necessary.– Input Matrix is taken back to Ideas.
© PTS Munich
Matlab Interface (Screenshot and Dialog Boxes)
MATLAB
© PTS Munich
Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
© PTS Munich
Adequate removal of detrimental substances
Minimum wear and tear
Optimally matched process stages
Low energy and chemicals
consumption
Properly dimensioned stock and water chests
Low stickies and ink content of the
finished stock
Finished stock with high product suitability
Minimum fibre losses
Controlled furnishOptimum deashing
Reliable HD cleaningReliable process
control
Appropriate reject and water management
Critical Performance Criteria of a Stock Preparation Plant
© PTS Munich
A Holistic Approach to Optimize Stock Preparation Plants
Concept development
Concept development
SimulationSimulation
Action planAction plan
CircuitryCircuitry
SamplingSampling
AnalyticsAnalytics
VolumesVolumes
Automation conceptAutomation concept
Process parameterProcess parameter BalancesBalances
Cost-Benefit analysisCost-Benefit analysis
BenchmarksBenchmarks
Sankey-DiagramsSankey-Diagrams
ProfilesProfiles
FlowsFlows
Efficiency valuesEfficiency values
ModellingModelling
Fibres/Fillers/additvesFibres/Fillers/additves
Production dataProduction data
Stickies controlStickies control
RejectsRejects
Equipment dataEquipment data
Circuitry evaluationCircuitry evaluation
ToolsData
acquisition
Modules
Yield improvementYield improvement
Deinking / BrightnessDeinking / Brightness
FaserpotentialFaserpotential
Cost-Benefit Calculation
Cost-Benefit Calculation
Basis
Benchmarking
Cost reduction
Capacity analysis
Basis
Benchmarking
Cost reduction
Capacity analysis
Equipment evaluationEquipment evaluation
®
© PTS Munich
Balancing of Stickies
The key issue is: to pay less attention on the stickies loading
but to look more in detail at the stickies load:
(stickies loading x mass flow (fibres + ash) = stickies load)
The load is what the paper machine gets
To know were the stickies load (expressed in m²/h) is going within the stock preparation helps to find out at which location it is most effective to start optimising the plant.
)1(min²
min²106 Equationmkgkg
mm
© PTS Munich
Solids and Stickies Balance - Coarse Screening
5,2 m²/min
1,0 m²/min
Fibersorters 1A and 1B
1. Stage
6,3 m²/min
688 kg/min
solids flow [kg/min]
macro-stickies load (INGEDE Method 4) [m²/min]
Dumpchest
Fibersorter2. Stage
Omnifractorchest
Rejectsorter
RejectFine screening
3rd stage
Deflaker
1,1 m²/min
146 kg/min
21 kg/min
4,1 m²/min
125 kg/min
542 kg/min
1,8 m²/min
33 kg/min
8,0 m²/min
700 kg/min
21 kg/min
0,2 m²/min
9 kg/min
2,5 m²/min
8,0 m²/min
6,3 m²/min
4,1 m²/min
2,5 m²/min
rejectfine screening
3rd stage
1,8 m²/min
© PTS Munich
Solids and Stickies Balance - Fine Screening
1,3 m 2/min
2 1,0 m /min
3. StageII 0,20 mm
1. Stage fine
screeningII 0,20 mm
8,0 m 2/min
Disperging
Fractionation
II 0,20 mm
2. Stage fine
screeningII 0,20 mm
700 kg/min
6,7 m 2/min
292 kg/min
408 kg/min
132 kg/min
160 kg/min
5,7 m 2/min
101 kg/min
1,4 m 2/min
59 kg/min
4,3 m 2/min
0,2 m 2/min
38 kg/min
2,6 m 2/min
271 kg/min
Long fibre
chest
solids flow [kg/min]
macro-stickies load (INGEDE Method 4) [m²/min]
short fibre
21 kg/min
4,1 m 2/min
to reject sorter coarse screening
4,1 m²/min
21 kg/min
to rejectsortercoarse screening
4,1 m²/min
to rejectsortercoarse screening
1,3 m²/min
1,4 m²/min
0,2 m²/min
1,0 m²/min
total3,9 m²/min
short fibre
long fibre
8,0 m²/min
© PTS Munich
Object: Sorter
Status of the object– Under development– Steady state– Hierarchical object
Operational features– Improved sorter object– Separation of 8 components– 5 options to calculate separation rates – Option to enter separation rates directly– Visibility of all calculated results– Integrated error display
© PTS Munich
Internal Dialogs of the Object Sorter
© PTS Munich
Selection of the Mode of Operation
Option Input valuesEquations to calculate
Separation rate E i,R
1 Ei,R, EWater,R -
2CA, CR, und S i,A
oder S i,R
3Qi, RW, and T or
CA oder C R
4 Qi, CA, C R
5 Qi, RV, C A, CR
6 Qi, RW, P
wIi
RiRi R
S
SE
,
,,
Wii
WR,i
RQQ1
RE
Wii
WR,i
RQQ1
RE
iV
A
RVi
A
RV
R,i
Q1RC
CRQ1
C
CR
E
Wii
WR,i
RQQ1
RE
© PTS Munich
event
Discrete/Cont.Executive
ERRORS
Cp
IDEAS 3.0.0 IDEAS MP-1
m/hfor
IDEAS MP-1
R
A
I
W
DS A
Display
Master
AA
KW 1
FB & P
R
AI
DR
PS
PS
R
AA
I
W
VS
DR Rejekt
VS Rejekt
PS Rejekt2
MM
PP
RR11
22
BK
SRP1 BK
SRP2 BK
PS Rejekt1
AB = Ableerbütte
BT = Bleichturm
C = Contaminex
DR = Dickstoffreiniger
DSP = Doppelsiebpresse
DS = Dünnstoffschlitzsortierung
F = Fiberizer
FB = Förderband
FGB = Flotations-Gutstoffbehälter
FW = Frischwasser
GSB = Gutstoffbehälter
KF = Klarfiltrat
KW = Klarwasserbehälter
M = MERI
NF = Nachflotation
P = Pulper
PM = Papiermaschine
PS = Pulpsortierung
PW = Presswasser
S = Sortiertrommel
SchB = Schaumbehälter
SB = Schlammbehälter
SE = Schlammaufbereitung
SM = Schlamm-MERI
SRP = Schneckenrejektpresse
SSB = Spuckstoffbehälter
SSP = Schlammschneckenpresse
SF = Scheibenfilter
SP = Schneckenpresse
ST = Stapelturm
SZ = Scheibenzerfaserer
TF = Trubfiltrat
VB = Verdünnungsbütte
VFP = Primäre Vorflotation
VFS = Sekundäre Vorflotation
VS = Vorsortierung
VST = Vorseihtisch
VW = verdunstetes Wasser
WT = Wasserturm
ZB = Zwischenbütte
PS Rejekt2
SchB
DS B Rejekt
DS B Rejekt
PP
SS
11
22
RR
DS Rejekt
DS Rejekt
P KW2
VF Rejekt
MM
PP
RR11
22
BK
R
A
I
W
DS B
PP
SS
11
22
RR
M
SP
DR Rejekt
MM
PP
RR11
22
Schlamm
MM
PP
RR11
22
MM
PP
RR11
22
BK
VF Rejekt
R
A
SM
PS Rejekt1
PM_ARA
KF
I
TF
SF 1
MM
PP
RR11
22
BKC1C1
C2C2PP
11
22
VSTSSA BK
MM
PP
RR11
22
BK
C1C1
C2C2PP
11
22
SSP
C1C1
C2C2PP
11
22
SRP 1MM
PP
RR11
22
VS Rejekt
RA
T
SA
SB
VFP A & B
NF Rejekt
R SA
SB
VFS A & B
C1C1
C2C2PP
11
22
SRP 2
SB 1
SB 2
PP
SS
11
22
RR
SRP1 BK
SRP2 BK
SSA BK
C1C1
C2C2PP
11
22
SSA 2
Container(Deponieabfall)
MM
PP
RR11
22
BKP
ARA
M Flotat
MM
PP
RR11
22
BK
Example Model Built with Sorter Object
AcceptOutAcceptOut
Con1InCon1In Rejekt1OutRejekt1Out Rejekt2OutRejekt2Out
R
A
I
F
R
A
I
C
R
AI
S
MM
PP
RR11
22
Con1InCon1In
ConAOutConAOut
ConROutConROutR
A
I
DS 3R
A
I
DS 1
R
A
I
DS 2
MM
PP
RR11
22
112233
PP
55
66
77
88
44 112233
PP
55
66
77
88
44
ConTFInConTFIn
© PTS Munich
Example: Optimization of Van Houtum Mill
One week of process analysis produced a clear evaluation of the actual situation:
- Efficiency of stock preparation relating to macro stickies separation was only about 40%
- desired value: 80 - 90%. Concept development: Small modifications and
operational changes Simulated separation rate in screening stage 70% Concept applied Achieved separation rate: 70%
© PTS Munich
Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
© PTS Munich
0
500
1000
1500
2000
2500
3000
0:00 0:30 1:00 1:30 2:00
00,51
1,522,53
3,544,5
Startup
0
10
20
30
40
50
60
70
80
90
100
0:00 0:30 1:00 1:30 2:00
Lev
el i
n %
AP 32
AP 12
Bütte 1
Bütte 2
MB DE
MB UL
Gbb
AS-Bütte
Puffer
process
data
simulated
data
Startup
0,0%
10,0%
20,0%
30,0%
40,0%
50,0%
60,0%
70,0%
80,0%
90,0%
100,0%
20:00 20:30 21:00 21:30 22:00 22:30
Lev
el i
n %
AP 32
AP 12
Bütte 1
Bütte 2
MB DE
MB UL
Gbb
AS-Bütte
Puffer
Start up: Comparison of Simulated and Original Data
© PTS Munich
Evaluation of Dynamic Simulation Models – Software Development
Objective:Development of a MATLAB based software tool, suitable for evaluation of the forecast accuracy of a simulation model
Status of the object–Under development
Operational features–Data logging in Ideas (with Matlab Link)–Analysis of time trends (Matlab)–Scenario management (Matlab)–Automatic evaluation of new calculated scenarios (Matlab)
© PTS Munich
Evaluation of Dynamic Simulation Models – Screenshot
Under development
© PTS Munich
Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
© PTS Munich
Modelling the Properties of Paper: CAPD
Paper & board Fibres Process Who Target
Purchase Detecting cost-effective fibres
Paper mill Controlling process conditions
R & DDesign of innovative paper & board property combinations
fix
fix flow
flow
flow flow
?
?
?
Fibres & materials
Process parameters
Physical paper & board properties
C A
P D
A software-based modular tool box for pulp and paper enterprises that helps to predict variations in resulting physical
paper properties based on fibre characteristics and process parameters.
Optimisation under constraints
Reverse engineering
© PTS Munich
The Dependency Tree - a Kind of Neural Network
FLD dfSR fP
RHO
WSR SR0fRho
AVLINV
OG
SSA
H
RBA
FZFBA
C Z
P
b
BL
FWD
EPS
fD
WF WP
TI
Fibre char. Fibre and network constants Paper propertiesFibre Length Distrib. (FLD), Fibre Width Distr. (FWD), SR-
value (SR)
App. Density (RHO), Strain to rupture (EPS), Breaking length (BL), Tear index (TI)
FF
Coarseness (C), Shear strength (b), Zero span tensile (Z), Fibre wall density (W), SR0, Fibre strain (F), Fibre shear modul (F),
Packaging (fRho, fP), Dewatering (fSR), Statistic of failure (fD), Bonding distance (d)
WRV
© PTS Munich
CAPD: Simulation of Paper Sheet Strength Properties
727 743 759 775 791 808 824 840 856 872 88820
25
30
35
40
45
50
55
60
65
70
[g/cm3]
Av. Fibre length [m]
SR [o]
Apperent density
0.75-0.80
0.70-0.75
0.65-0.70
0.60-0.65
0.55-0.60
0.50-0.55
727 743 759 775 791 808 824 840 856 872 88820
25
30
35
40
45
50
55
60
65
70
[m]
Av. Fibre length [mm]
SR [o]
Breaking length
7000-7500
6500-7000
6000-6500
727 743 759 775 791 808 824 840 856 872 88820
25
30
35
40
45
50
55
60
65
70
[%]
Av. Fibre length [m]
SR [o]
Strain to rupture
3.75-4.00
3.50-3.75
3.25-3.50
3.00-3.25
727 743 759 775 791 808 824 840 856 872 88820
25
30
35
40
45
50
55
60
65
70
[mNm2/g]
Av. Fibre length [m]
SR [o]
Tear Index (E.)
11.50-12.00
11.00-11.50
10.50-11.00
10.00-10.50
9.50-10.00
9.00-9.50
8.50-9.00
8.00-8.50
Refining path
© PTS Munich
Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
© PTS Munich
Example Project to Model the Wet End Chemistry
Extensive paper machine trials– Online data vs. laboratory results (A60 value/Cobb60)
Correlation analyses of inputs and outputsCreation of a model for individual trial / overall
modelimportant parameters
– steam consumption after dryer section– moisture in front of the size press– consumption of sizing agent
Simulation results available as soft sensor Optimisation calculations can be used to minimize
cost for sizing (simulated annealing)
© PTS Munich
Correlation Map to Analyse Results of the Paper Machine Trials
Time Resin sizePAC Chalk KaolinWet end starch Retention agent Microparticles Broke treatment Starch solution Moisture before size press Grammage Moisture pope reel Ash Paper output Stean consumption Surface starch 1 Surface starch 2 Surface sizing Sizing Sizing factor Cobb water 60 top side Cobb water 60 wire side W top side Max top side A60 top side W web side Max web side A60 web side Poly Dadmac 0.001n white water Retention Resin size of paper Resin size of head box Resin size of white water
tp9p10p11p12p13p14p15p19p25p34p35p36p37p41p92p117p119p127p146p147p148p149p177p178p179p180p181p182p183p184p185p186p187
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Optimisation of Sizing by Simulated Annealing
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Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
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A Look Into the Future
PTS will have available a tool to quantitatively evaluate the accuracy of dynamic models.
Relevance to the “IDEAS world”?
CAPD will, within the next years, be ready to be used for paper product development (virgin fiber based papers).
Should the results be integrated into Kodiak?
How do we get to accurate (physical) wet end chemistry models?
Will future IDEAS developments be supporting this?
© PTS Munich
Agenda
The PTS simulation approach Use of IDEAS in paper mill optimisation projects
– Steady state water loop optimisation – The “loop” object– Heat balance optimisation– Dynamic stock and water optimisation – The Matlab link– Stock preparation – Extension of the “sorter” object
Beyond IDEAS– Measurement of the accuracy of dynamic models– Modelling the properties of paper: CAPD– Modelling the wet end chemistry
A look into the future European networking activities – COST E36
© PTS Munich
What is COST?
COST …
– is one of the oldest funding mechanisms of the European Commission
– has been established in order to promote the exchange of scientific knowledge within the European Community
– is a predecessor of the Networks of Excellence (NoE) defined within the 6th Framework Programme
– is funded by the European Community within the 6th Framework Programme and managed by the European Science Foundation (ESF)
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Objectives and Benefits of Action E36
The main objective of the Action is to promote the development and application of modelling and simulation techniques in pulp and paper manufacturing processes.
The main benefit will be a better understanding of the mechanisms of the processes and their control loops. This will help to find solutions for currently pending problems in the paper industry: improving the paper quality, optimising the wet end chemistry, enhancing the runnability and reducing emissions by improving process design, process monitoring and decision support during operation.
In the long run this action should also contribute to
designing superior or new product properties.
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Key Figures E36
13 participating countries: Austria, Belgium, Denmark, Finland, France, Germany, Netherlands, Norway, Slovakia, Slovenia, Spain, Sweden, United Kingdom
Members MC: 17
Members WG’s: > 40 (in 3 WG’s)
Duration: 4 years (22.1.2004 – 21.1.2008)
Budget: approx. 60 T€ per year
Estimated cost of the activities carried out in the course of the action: 20 M€ (180 person-years)
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Activities
Action started with a kick off meeting in the beginning of 2004
Presentations given at the Munich conference in March 2004 provide a good insight into the simulation knowledge available in p&p
Young scientist exchange is financed and promoted by the action through short term scientific missions
Working groups are currently defining their thematic focus within the frame of the memorandum
Workshops and Conferences will be organised in order to further promote the knowledge exchange
Dedicated reports and books are currently elaborated and will be published soon
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Working Groups
A Modelling and simulation of the pulping and the paper production processes (eg. R&D tool, optimal process design, operator training, trouble shooting)
B Online simulation of pulp and paper production processes (monitoring, operations decision support, model based control)
C Assessment of simulation software in the pulp and paper industry and recommendations on further developments (integration aspects included)
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Working Group C: Specific Results
WG C will provide the following specific results– A survey on the current use of simulation software– Recommendations on the exchange of know how
contained in models– A report concerning recommendations on suitable
software tools and requirements for further software development
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E 36: Whom to Contact
www.costE36.org
Chairman: Dr. Johannes Kappen (GE)([email protected])
Vice Chairman: Prof. Risto Ritala (FI)([email protected])
WG A leader: Dr. Jussi Manninen (FI)([email protected])
WG B leader: Prof. Erik Dahlquist (SE)([email protected])
WG C leader: Prof. Carlos Negro (ES)([email protected])
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Summary
PTS has worked with IDEAS software successfully during the past years
IDEAS is our tool to support all process optimization projects
Thus IDEAS is the most important basis PTS has built its process optimization upon and will be in the future
Added value is included by combining IDEAS with Matlab.
In the long run PTS is interested in including these functionalities into IDEAS
PTS currently brings together European researchers in the field of simulation (COSTE36)